论文标题
连接Web事件预测与异常检测：使用自我监督的神经网络对企业Web应用程序的案例研究
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks
论文作者
论文摘要
最近，Web应用程序已在企业中广泛使用，以帮助员工提供有效，有效的业务流程。在企业Web应用程序中预测即将到来的Web事件在许多方面都可以有益，例如有效的缓存和建议。在本文中，我们在企业Web应用程序中提出了一种Web事件预测方法，即“深层”，以更好地异常检测。 DeepEvent包含三个关键功能：特定网络的神经网络，以考虑顺序Web事件的特征，自我监督的学习技术以克服标记数据的稀缺性以及序列嵌入技术以整合上下文事件并捕获依赖性的依赖性。我们对从六个现实世界企业Web应用程序收集的Web事件进行了深入评估。我们的实验结果表明，DeepEvent在预测顺序Web事件并检测基于Web的异常方面有效。 DeepEvent为研究人员和从业人员提供了一个基于上下文的系统，以更好地预测情境意识的网络事件。
Recently web applications have been widely used in enterprises to assist employees in providing effective and efficient business processes. Forecasting upcoming web events in enterprise web applications can be beneficial in many ways, such as efficient caching and recommendation. In this paper, we present a web event forecasting approach, DeepEvent, in enterprise web applications for better anomaly detection. DeepEvent includes three key features: web-specific neural networks to take into account the characteristics of sequential web events, self-supervised learning techniques to overcome the scarcity of labeled data, and sequence embedding techniques to integrate contextual events and capture dependencies among web events. We evaluate DeepEvent on web events collected from six real-world enterprise web applications. Our experimental results demonstrate that DeepEvent is effective in forecasting sequential web events and detecting web based anomalies. DeepEvent provides a context-based system for researchers and practitioners to better forecast web events with situational awareness.
